In the past 10 years, machine learning and Artificial Intelligence (AI) have shown tremendous progress
The recent success can be attributed to:
Explosion of data
Cheap computing cost - CPUs and GPUs
Improvement of machine learning models
Much of the current excitement concerns a subfield of it called “deep learning”.
Human Brain
Neural Networks
Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges.
Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes.
Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers.
Each node (also called a neuron) in the hidden and output layers has a classifier.
Neural Network Layers
Deep Learning
Deep learning refers to artificial neural networks that are composed of many layers.
It’s a growing trend in Machine Learning due to some favorable results in applications where the target function is very complex and the datasets are large.
Deep Learning: Benefits
Robust
No need to design the features ahead of time - features are automatically learned to be optimal for the task at hand
Robustness to natural variations in the data is automatically learned
Generalizable
The same neural net approach can be used for many different applications and data types
Scalable
Performance improves with more data, method is massively parallelizable
Deep Learning: Weaknesses
Deep Learning requires a large dataset, hence long training period.
In term of cost, Machine Learning methods like SVMs and other tree ensembles are very easily deployed even by relative machine learning novices and can usually get you reasonably good results.
Deep learning methods tend to learn everything. It’s better to encode prior knowledge about structure of images (or audio or text).
The learned features are often difficult to understand. Many vision features are also not really human-understandable (e.g, concatenations/combinations of different features).
Requires a good understanding of how to model multiple modalities with traditional tools.
Deep Learning: Applications
Deep Learning: R Libraries
MXNet: The R interface to the MXNet deep learning library.
darch: An R package for deep architectures and restricted Boltzmann machines.
deepnet: An R package implementing feed-forward neural networks, restricted Boltzmann machines, deep belief networks, and stacked autoencoders.
h2o: The R interface to the H2O deep-learning framework.
MXNet
Founded by: Uni. Washington & Carnegie Mellon Uni (~1.5 years old)
Supports most OS: Runs on Amazon Linux, Ubuntu/Debian, OS X, and Windows OS
State of the art model support: Flexible and efficient GPU computing and state-of-art deep learning i.e. CNN, LSTM to R.
Ultra Scalable: Seamless tensor/matrix computation with multiple GPUs in R.
Ease of USe: Construct and customize the state-of-art deep learning models in R, and apply them to tasks, such as image classification and data science challenges
Multi-language: Supports the Python, R, Julia and Scala languages
Ecosystem: Vibrant community from Academia and Industry
MXNet Architecture
You can specify the Context of the function to be executed within. This usually includes whether the function should be run on a CPU or a GPU, and if you specify a GPU, which GPU to use.
MXNet R Package
The R Package can be downloaded using the following commands:
